Bibliographic record
Abstract
In 2008, People for Education, an Ontario-based parent-led organization, hosted eight policy dialogues with citizens about possibilities for the province‟s public schools. Policy dialogues are conversations about policy issues, ideas, processes, and outcomes where participants share their knowledge, perspectives, and experiences. In small groups dialogue participants were asked to share their ideas about the ideal school of the future. Participants‟ ideas were recorded by a facilitator. Following each dialogue participants were asked to complete a short survey about their experience. Fifteen sets of facilitators‟ notes and 46 participant surveys were analyzed in this study. The data show that participants‟ ideal school emphasizes variety, flexibility, caring relationships, individualized programs, and community connections. Importantly, policy dialogues promote participants‟ cognitive, affective, and behavioural engagement with education policy. Finally, policy dialogues enhance democracy in education by providing opportunities for critical examination of public policy by ordinary citizens who are viewed as important policy actors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.048 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.036 | 0.042 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".